Cichocki Andrzej
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:4466-9. doi: 10.1109/EMBC.2015.7319386.
In this paper, we first presents a high accuracy epileptic electroencephalogram (EEG) classification algorithm. EEG data of epilepsy patients are preprocessed, segmented, and decomposed to intrinsic mode functions, from which features are extracted. Two classifiers are trained based on linear discriminant analysis (LDA) to classify EEG data into three types, i.e., normal, spike, and seizure. We further in-depth investigate the changes of the decision values in LDA on continuous EEG data. An epileptic EEG visualization and sonification algorithm is proposed to provide both temporal and spatial information of spike and seizure of epilepsy patients. In the experiment, EEG data of six subjects (two normal and four seizure patients) are included. The experiment result shows the proposed epileptic EEG classification algorithm achieves high accuracy. As well, the visualization and sonification algorithm exhibits a great help in nursing seizure patients and localizing the area of seizures.
在本文中，我们首先提出了一种高精度的癫痫脑电图（EEG）分类算法。对癫痫患者的脑电图数据进行预处理、分段，并分解为固有模态函数，从中提取特征。基于线性判别分析（LDA）训练两个分类器，将脑电图数据分为三种类型，即正常、尖峰和癫痫发作。我们进一步深入研究了LDA对连续脑电图数据决策值的变化。提出了一种癫痫脑电图可视化和可听化算法，以提供癫痫患者尖峰和癫痫发作的时间和空间信息。在实验中，纳入了六名受试者（两名正常人和四名癫痫患者）的脑电图数据。实验结果表明，所提出的癫痫脑电图分类算法具有很高的准确率。此外，可视化和可听化算法在护理癫痫发作患者和定位癫痫发作区域方面表现出很大的帮助。
